Therefore, it becomes increasingly attractive to process data locally at edge devices.
This has led to the emergency of distributed optimization methods.
In distributed optimization, each node can compute on its own data and sends the results to its neighbours or a central node.
Distributed optimization has many applications, such as user selection optimization, resource allocation optimization, trajectory optimization, and distributed machine learning design[1]}.
